The objective of this paper is texture classification from a single
image acquired under unknown viewpoint and illumination. The method
is based on the statistical distribution of filter responses in a low
dimensional space.
We compare two methods of representing the filter response
distribution. The first is to store the entire response as a PDF, the
second is to learn representative texture elements (\emph{textons}) by
clustering, and represent the texture by their distribution. This
effectively compares the PDF approach of Konishi and Yuille (CVPR
2000), to the texton approach of Cula and Dana (CVPR 2001) and Varma
and Zisserman (ECCV 2002). The methods are assessed by classifying the
material images present in the Columbia-Utrecht database, and very
accurate results are obtained.
We also describe preliminary results on estimating orientation
co-occurrence statistics. These statistics may be used to augment
the texton representation and improve classification performance.